Hemoglobin A1C Variability as a Risk Factor for Diabetes Complications

持续性高血糖预示微血管并发症的发展(例如视网膜病变和 糖尿病患者的肾脏病变)。然而，血糖控制与器官损伤的关系 是很复杂的。降低血红蛋白A1c可预防或延缓微血管并发症，但 心血管疾病(CVD)和死亡率受到的影响不一致。因此，有必要开发新的 仅有A1C之外的质量措施，以更好地识别和治疗有并发症和死亡风险的患者。 以A1c随时间的波动来衡量的A1c变异性是一个很好的候选指标。多项研究表明， 糖化血红蛋白变异性增加与微血管疾病和心血管疾病显著相关。而A1c 变异性携带着重要的风险信息，标准差等方差度量可能不会 临床上很直观。因此，我们的目标是开发一种新的A1c变异性的质量度量-A1c时间范围 (TIR)-这有助于临床医生和患者以平衡长期利益和风险的方式控制A1c。我们 将A1c TIR定义为患者A1c水平处于特定目标范围的天数百分比，基于 他们的临床特点和临床实践指南。我们将对A1c TIR进行概括性的研究 来自退伍军人事务部和Kaiser Permanente的全国范围内超过365,000名患者的样本。 我们将应用先进的统计方法，通过使用仪器来严格控制选择偏差 可变设计，包括工艺质量控制。这些方法使我们能够在 TIR和新的糖尿病并发症的风险。我们将研究A1c TIR的预测因素，我们假设将 受提供者执业模式、个体患者水平特征和药物的影响。我们将测试 假设较高的A1c-TIR可降低糖尿病并发症的风险。我们亦会研究 反向-A1c时间超出范围(TOR)，对高于(TOR[高])和低于(TOR[高])的偏差感兴趣 范围(TOR[低])，以确定两者是否与微血管或大血管并发症唯一相关。 然后我们将调查临床因素、低血糖事件和A1c的快速下降，作为 TIR与不良结局的关系。两者都与死亡和糖尿病的早期恶化有关。 分别是并发症。这项研究将通过开发一种新的质量衡量标准来促进糖尿病的护理 以风险分层的方式识别患者，并帮助临床医生根据患者的 独特的护理目标。这样的新措施将由临床医生在护理地点和医疗保健部门使用 人口健康管理系统。

Persistent hyperglycemia predicts the development of microvascular complications (e.g. retinopathy and nephropathy) in patients with diabetes. However, the relationship between glucose control and organ damage is complex. Reducing hemoglobin A1c (A1c) prevents or delays microvascular complications but cardiovascular disease (CVD) and mortality are inconsistently affected. Thus, there is a need to develop new quality measures beyond A1c alone to better identify and treat patients at risk for complications and mortality. A1c variability, as measured by fluctuations in A1c over time, is a strong candidate. Several studies show a significant relationship between increased A1c variability and microvascular disease and CVD. While A1c variability carries important risk information, variance measures such as standard deviation, may not be clinically intuitive. Thus, our goal is to develop a new quality measure of A1c variability – A1c time in range (TIR) – that helps clinicians and patients control A1c in a way that balances long-term benefits and risks. We will define A1c TIR as the percentage of days a patient's A1c levels are in a specific target range, based on their clinical characteristics and clinical practice guidelines. We will study A1c TIR in a generalizable nationwide sample of over 365,000 patients from the Department of Veterans Affairs and Kaiser Permanente. We will apply advanced statistical methods that stringently control for selection bias by using an instrumental variable design, including process quality controls. These methods allow us to draw causal inferences between TIR and risk of new diabetes complications. We will study the predictors of A1c TIR, which we hypothesize will be affected by provider practice patterns, individual patient-level characteristics and medications. We will test the hypothesis that higher A1c TIR confers lower risk of diabetes complications. We will also study the converse – A1c time out-of-range (TOR), with interest in deviations both above (TOR [high]) and below the range (TOR [low]) to determine if either is uniquely associated with micro- or macrovascular complications. Then we will investigate clinical factors, hypoglycemic events and rapid declines in A1c, as mediators of the relationship between TIR and adverse outcomes. Each is linked to mortality and early worsening of diabetes complications, respectively. This study will advance diabetes care by developing a novel quality measure that identifies patients in a risk-stratified way and helps clinicians tailor diabetes treatment based on a patient's unique goals of care. Such a new measure will be used by clinicians at the point-of-care and by healthcare systems for population health management.